Paper

  • The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof

    Stefanie Jegelka, Derek Lim, Haggai Maron, Theo Putterman, Robin Walters p28322-28358 from Advances in Neural Information Processing Systems 37
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  • The Energy Storage Capabilities of Single and Mixture Sorbent Salts Impregnated Pumice and Anodic Aluminium Oxide Based Composite Materials

    ECOS 2023

    The Energy Storage Capabilities of Single and Mixture Sorbent Salts Impregnated Pumice and Anodic Aluminium Oxide Based Composite Materials

    Behiye Yüksel, Zafer Utlu, Esra Ayan p2322-2329 from 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023)
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  • THE ENERGY SYSTEMS ANALYSIS WITH A MULTI-CRITERIA DESIGN TOOL TO DECARBONIZE MARITIME TRANSPORT

    ECOS 2024

    THE ENERGY SYSTEMS ANALYSIS WITH A MULTI-CRITERIA DESIGN TOOL TO DECARBONIZE MARITIME TRANSPORT

    Giaime Niccolò Montagna, Simone Piccardo, Matteo Passalacqua, Daria Bellotti, Luca Mantelli, Massimo Rivarolo p216-227 from 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)
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  • The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games

    Victor Boone, Panayotis Mertikopoulos p78533-78557 from Advances in Neural Information Processing Systems 36
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  • The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains

    Benjamin Edelman, Ezra Edelman, Surbhi Goel, Eran Malach, Nikolaos Tsilivis p64273-64311 from Advances in Neural Information Processing Systems 37
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  • The Exact Sample Complexity Gain from Invariances for Kernel Regression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Exact Sample Complexity Gain from Invariances for Kernel Regression

    Stefanie Jegelka, Behrooz Tahmasebi p55616-55646 from Advances in Neural Information Processing Systems 36
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  • The Exergy Footprint and the Resource Cost of Externalities

    ECOS 2023

    The Exergy Footprint and the Resource Cost of Externalities

    Enrico Sciubba p34-41 from 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2023)
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  • The Expressive Capacity of State Space Models: A Formal Language Perspective

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Expressive Capacity of State Space Models: A Formal Language Perspective

    Michael Hahn, Yash Sarrof, Yana Veitsman p41202-41241 from Advances in Neural Information Processing Systems 37
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  • The expressive power of pooling in Graph Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The expressive power of pooling in Graph Neural Networks

    Filippo Maria Bianchi, Veronica Lachi p71603-71618 from Advances in Neural Information Processing Systems 36
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  • The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More

    Diane Bouchacourt, Mark Ibrahim, Ouail Kitouni, Niklas Nolte, Mike Rabbat, Adina Williams p112329-112355 from Advances in Neural Information Processing Systems 37
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  • The Fairness-Quality Tradeoff in Clustering

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Fairness-Quality Tradeoff in Clustering

    Rashida Hakim, Christos Papadimitriou, Ana-Andreea Stoica, Mihalis Yannakakis p117509-117542 from Advances in Neural Information Processing Systems 37
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  • The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks

    Lénaïc Chizat, Praneeth Netrapalli p62362-62383 from Advances in Neural Information Processing Systems 37
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